Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "83" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 40 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 38 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459855 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 4.58% | 0.00% | -0.053121 | -0.145843 | 2.502452 | -0.312378 | -0.764800 | 0.416921 | -0.894721 | -0.781961 | 0.7098 | 0.7284 | 0.4249 | 1.432737 | 1.393088 |
| 2459854 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.93% | 0.00% | -0.214619 | -0.241551 | 2.939787 | 0.486277 | -0.751163 | -0.602595 | -0.814418 | -0.541099 | 0.7243 | 0.7445 | 0.4329 | 1.430388 | 1.392450 |
| 2459853 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.290959 | 0.001044 | 4.153513 | 1.075058 | -0.997299 | -0.419048 | -0.716269 | -0.279600 | 0.7479 | 0.7056 | 0.4184 | 2.973632 | 2.808683 |
| 2459852 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.65% | 1.62% | 0.572050 | 0.533210 | 3.700927 | 0.780941 | 0.941216 | 0.219202 | 2.441535 | 0.510700 | 0.8312 | 0.8383 | 0.2410 | 2.816569 | 3.031596 |
| 2459847 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.809122 | 0.782127 | 6.422891 | 2.373292 | -0.594362 | -0.531833 | -0.691933 | 0.086103 | 0.7361 | 0.7006 | 0.4219 | 3.012130 | 2.795108 |
| 2459846 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.551615 | 1.171436 | 4.966292 | 1.957238 | 2.324351 | 1.556854 | -0.897180 | -0.252425 | 0.8486 | 0.7001 | 0.4682 | 3.268942 | 3.306635 |
| 2459845 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.030750 | 1.490352 | 9.017231 | 4.296850 | -0.712575 | 0.755041 | -0.737789 | -1.034129 | 0.7321 | 0.7473 | 0.3714 | 8.377589 | 13.472851 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 2.677287 | 0.615085 | 15.694559 | 13.506567 | 3.100808 | 2.967580 | -0.686867 | -0.874124 | 0.0264 | 0.0265 | 0.0007 | nan | nan |
| 2459843 | digital_ok | 0.00% | 0.66% | 0.66% | 0.00% | 16.30% | 0.00% | 0.890671 | 0.976080 | 1.378174 | 2.637013 | -0.810607 | -1.175658 | -0.863822 | -0.668294 | 0.7444 | 0.7543 | 0.3732 | 1.754726 | 1.667221 |
| 2459840 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.537190 | 0.560437 | 0.653897 | 1.236908 | 0.892992 | 0.086882 | -1.509530 | -2.347300 | 0.0252 | 0.0256 | 0.0017 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | 0.123514 | -0.110068 | 13.234670 | 14.288316 | 3.845041 | 3.614524 | 7.665696 | 4.098328 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.134754 | 0.372845 | 1.967208 | 3.406256 | -1.693491 | -0.499379 | -0.962333 | -0.970755 | 0.7586 | 0.7340 | 0.3932 | 1.837344 | 1.493494 |
| 2459835 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.058612 | -0.089545 | 1.415524 | 1.812162 | -0.510581 | 1.151851 | -0.559309 | -0.877750 | 0.0414 | 0.0372 | 0.0005 | nan | nan |
| 2459833 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.574479 | 0.559474 | 2.209368 | 3.098151 | -0.088507 | 1.496214 | -0.824716 | -1.267363 | 0.0430 | 0.0647 | 0.0090 | nan | nan |
| 2459832 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.099146 | 1.273175 | 1.315937 | 3.051195 | -0.651990 | 1.157724 | -0.704653 | -1.115100 | 0.8148 | 0.5759 | 0.5465 | 1.598396 | 1.630285 |
| 2459831 | digital_ok | 100.00% | 100.00% | 55.11% | 0.00% | - | - | 0.110682 | 3.345320 | 16.530689 | 17.632962 | 6.286092 | 7.427778 | -1.108251 | -2.007899 | 0.1283 | 0.3308 | 0.0284 | nan | nan |
| 2459830 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.530522 | 1.828379 | 2.269046 | 4.510849 | 0.242749 | 1.416139 | -0.735016 | -1.415053 | 0.8166 | 0.5969 | 0.5174 | 4.268482 | 4.637247 |
| 2459829 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.461122 | 1.515704 | 1.845289 | 4.424291 | -0.983373 | -0.246316 | -0.065852 | -0.945488 | 0.7675 | 0.7012 | 0.3847 | 8.242352 | 8.135374 |
| 2459828 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.538611 | 1.684048 | 1.357299 | 3.255663 | 0.704613 | 2.019863 | -1.436626 | -2.258855 | 0.8140 | 0.5993 | 0.5072 | 1.598835 | 1.426338 |
| 2459827 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.254420 | 1.513665 | 2.925876 | 5.914086 | -0.784412 | -0.181068 | -0.669230 | -0.709139 | 0.7699 | 0.7070 | 0.3890 | 11.014514 | 9.833194 |
| 2459826 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.721276 | 1.976640 | 2.711984 | 5.517362 | 1.003490 | 1.839593 | -0.893805 | -1.442627 | 0.8083 | 0.6057 | 0.4968 | 7.555245 | 8.649175 |
| 2459825 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.082894 | 1.495909 | 1.249545 | 3.372131 | -0.302894 | 0.349071 | -0.889003 | -0.803950 | 0.8133 | 0.6331 | 0.4816 | 2.224819 | 2.144962 |
| 2459824 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.135685 | 0.368298 | 1.764234 | 4.634787 | -1.206101 | -0.359316 | -0.477453 | -0.402487 | 0.7391 | 0.7686 | 0.3521 | 6.357315 | 5.325988 |
| 2459823 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.001027 | 2.884907 | 2.433861 | 4.687827 | 1.513172 | 3.831000 | -0.653437 | -1.345082 | 0.7790 | 0.6899 | 0.4338 | 147.893082 | 62.287714 |
| 2459822 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.883061 | 2.438365 | 2.298280 | 4.713053 | 0.868873 | 1.980782 | -0.585487 | -0.882791 | 0.8095 | 0.6438 | 0.4795 | 5.325270 | 5.058635 |
| 2459821 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.985497 | 4.012967 | 2.259000 | 4.963449 | -0.468798 | 1.081793 | -1.584124 | -0.940632 | 0.8118 | 0.6645 | 0.4752 | 4.809398 | 4.177107 |
| 2459820 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.900803 | 1.981217 | 2.690570 | 5.329116 | 0.555087 | 3.188974 | -0.649889 | -1.045337 | 0.7797 | 0.7206 | 0.4028 | 4.206135 | 4.426662 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 2.079706 | 3.282957 | 1.526681 | 3.878312 | 0.666932 | 3.617099 | -0.749656 | -0.848056 | 0.8207 | 0.6970 | 0.4725 | 1.757359 | 1.672309 |
| 2459816 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.904574 | 2.251964 | 2.340664 | 4.994552 | 1.619464 | 3.129430 | -0.570299 | -0.888557 | 0.8488 | 0.6294 | 0.5697 | 3.530372 | 3.578635 |
| 2459815 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.160645 | 3.117079 | 2.078628 | 4.478247 | 1.030457 | 3.320691 | -1.152183 | -1.411663 | 0.8201 | 0.7127 | 0.4767 | 3.587119 | 3.852336 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.564933 | 4.631314 | 1.645771 | 4.022010 | 1.392464 | 8.277987 | 0.185755 | -0.345024 | 0.8015 | 0.7583 | 0.3749 | 14.922856 | 10.887344 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | ee Power | 2.502452 | -0.145843 | -0.053121 | -0.312378 | 2.502452 | 0.416921 | -0.764800 | -0.781961 | -0.894721 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | ee Power | 2.939787 | -0.241551 | -0.214619 | 0.486277 | 2.939787 | -0.602595 | -0.751163 | -0.541099 | -0.814418 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | ee Power | 4.153513 | 0.001044 | 0.290959 | 1.075058 | 4.153513 | -0.419048 | -0.997299 | -0.279600 | -0.716269 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | ee Power | 3.700927 | 0.572050 | 0.533210 | 3.700927 | 0.780941 | 0.941216 | 0.219202 | 2.441535 | 0.510700 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | ee Power | 6.422891 | 0.782127 | 0.809122 | 2.373292 | 6.422891 | -0.531833 | -0.594362 | 0.086103 | -0.691933 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | ee Power | 4.966292 | 1.551615 | 1.171436 | 4.966292 | 1.957238 | 2.324351 | 1.556854 | -0.897180 | -0.252425 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | ee Power | 9.017231 | 1.490352 | 2.030750 | 4.296850 | 9.017231 | 0.755041 | -0.712575 | -1.034129 | -0.737789 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | ee Power | 15.694559 | 2.677287 | 0.615085 | 15.694559 | 13.506567 | 3.100808 | 2.967580 | -0.686867 | -0.874124 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 2.637013 | 0.976080 | 0.890671 | 2.637013 | 1.378174 | -1.175658 | -0.810607 | -0.668294 | -0.863822 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 1.236908 | -0.537190 | 0.560437 | 0.653897 | 1.236908 | 0.892992 | 0.086882 | -1.509530 | -2.347300 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 14.288316 | -0.110068 | 0.123514 | 14.288316 | 13.234670 | 3.614524 | 3.845041 | 4.098328 | 7.665696 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 3.406256 | 0.372845 | 0.134754 | 3.406256 | 1.967208 | -0.499379 | -1.693491 | -0.970755 | -0.962333 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 1.812162 | -0.089545 | -0.058612 | 1.812162 | 1.415524 | 1.151851 | -0.510581 | -0.877750 | -0.559309 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 3.098151 | 0.559474 | -0.574479 | 3.098151 | 2.209368 | 1.496214 | -0.088507 | -1.267363 | -0.824716 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 3.051195 | 1.099146 | 1.273175 | 1.315937 | 3.051195 | -0.651990 | 1.157724 | -0.704653 | -1.115100 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 17.632962 | 0.110682 | 3.345320 | 16.530689 | 17.632962 | 6.286092 | 7.427778 | -1.108251 | -2.007899 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 4.510849 | 1.530522 | 1.828379 | 2.269046 | 4.510849 | 0.242749 | 1.416139 | -0.735016 | -1.415053 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 4.424291 | 1.515704 | 1.461122 | 4.424291 | 1.845289 | -0.246316 | -0.983373 | -0.945488 | -0.065852 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 3.255663 | 1.684048 | 1.538611 | 3.255663 | 1.357299 | 2.019863 | 0.704613 | -2.258855 | -1.436626 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 5.914086 | 1.254420 | 1.513665 | 2.925876 | 5.914086 | -0.784412 | -0.181068 | -0.669230 | -0.709139 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 5.517362 | 1.976640 | 1.721276 | 5.517362 | 2.711984 | 1.839593 | 1.003490 | -1.442627 | -0.893805 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 3.372131 | 1.495909 | 1.082894 | 3.372131 | 1.249545 | 0.349071 | -0.302894 | -0.803950 | -0.889003 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 4.634787 | 0.135685 | 0.368298 | 1.764234 | 4.634787 | -1.206101 | -0.359316 | -0.477453 | -0.402487 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 4.687827 | 2.884907 | 2.001027 | 4.687827 | 2.433861 | 3.831000 | 1.513172 | -1.345082 | -0.653437 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 4.713053 | 1.883061 | 2.438365 | 2.298280 | 4.713053 | 0.868873 | 1.980782 | -0.585487 | -0.882791 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 4.963449 | 4.012967 | 2.985497 | 4.963449 | 2.259000 | 1.081793 | -0.468798 | -0.940632 | -1.584124 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 5.329116 | 1.900803 | 1.981217 | 2.690570 | 5.329116 | 0.555087 | 3.188974 | -0.649889 | -1.045337 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 3.878312 | 2.079706 | 3.282957 | 1.526681 | 3.878312 | 0.666932 | 3.617099 | -0.749656 | -0.848056 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 4.994552 | 2.251964 | 1.904574 | 4.994552 | 2.340664 | 3.129430 | 1.619464 | -0.888557 | -0.570299 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Power | 4.478247 | 3.117079 | 2.160645 | 4.478247 | 2.078628 | 3.320691 | 1.030457 | -1.411663 | -1.152183 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | N07 | digital_ok | nn Temporal Variability | 8.277987 | 4.631314 | 3.564933 | 4.022010 | 1.645771 | 8.277987 | 1.392464 | -0.345024 | 0.185755 |